FORECASTING MULTIFRACTAL VOLATILITY PDF

This paper develops analytical methods to forecast the distribution of future returns for a new continuous-time process, the Poisson multifractal. The process . of Technology. Chapter 7: Thoroughly revised version from Journal of Econometrics,. , L. E. Calvet and A. J. Fisher. ‘Forecasting Multifractal Volatility,’ pp. Calvet and Fisher present a powerful, new technique for volatility forecasting that draws on insights from the use of multifractals in the natural sciences and.

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Laurent-Emmanuel Calvet 1 AuthorId: As the access to this document is restricted, you may want to look for a different version below or search for a different version of it. It also allows you to accept potential citations to this item that we are uncertain about.

Help us Corrections Found an error or omission? More about this item Statistics Access and download statistics. As the grid step size goes to zero, the discretized model weakly converges to the continuous-time process, implying the consistency of the density forecasts. The process captures the thick tails, volatility persistence and moment scaling exhibited by many financial time series.

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We introduce a discretized version of the model that has a finite state space and an analytical solution to the conditioning problem. This allows to link your profile to this item. Calvet, Laurent Fisher, Adlai. We introduce a discretized version of the model that has a finite state space and allows for an analytical solution to the conditioning problem. The process captures the thick tails, volatility persistence, and moment scaling exhibited by many financial time series.

The challenge in this environment is long memory and vollatility corresponding infinite dimension of the state space. The challenge in this environment is long memory and the corresponding infinite dimension of the state space.

Corrections All material on this site has been provided by the respective publishers and authors. Other versions of this item: We assume for simplicity that the forecaster knows the true forecastng process with certainty but only observes past returns.

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This paper forecasitng analytical methods to forecast the distribution of future returns for a new continuous-time process, the Poisson multifractal.

As the grid size goes to infinity, the discretized model weakly converges to the continuous-time process, implying the consistsency of the density forecasts. Laurent-Emmanuel Calvet 1 Adlai J. It can be interpreted as a stochastic volatility model with multiple frequencies and a Markov latent state.

Forecasting multifractal volatility

Full text for ScienceDirect subscribers only As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

Monday, Forcasting 17, – 4: Calvet Adlai Julian Fisher.

Paper This paper develops analytical methods to forecast the distribution of future returns for a new continuous-time process, the Poisson multi-fractal. Forecasting Long memory Multiple frequencies Stochastic volatility Weak convergence. It can be interpreted as a stochastic volatility model with multiple frequencies and a Markov latent state.